Full-coverage 250&thinsp;m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China

Chen, Xiangyue; Zuo, Hongchao; Zhang, Zipeng; Cao, Xiaoyi; Duan, Jikai; Zhu, Chuanmei; Zhang, Zhe; Wang, Jingzhe

doi:https://doi.org/10.5194/essd-14-5233-2022

Articles | Volume 14, issue 12

https://doi.org/10.5194/essd-14-5233-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-14-5233-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 14, issue 12

Data description paper

|

30 Nov 2022

Data description paper |

| 30 Nov 2022

Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China

Xiangyue Chen, Hongchao Zuo, Zipeng Zhang, Xiaoyi Cao, Jikai Duan, Chuanmei Zhu, Zhe Zhang, and Jingzhe Wang

Supplement

https://doi.org/10.5194/essd-14-5233-2022-supplement

Data sets

High-resolution and full coverage AOD downscaling based on the bagging model over the arid and semi-arid areas, NW China Xiangyue Chen https://doi.org/10.5281/zenodo.5727119

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Short summary

Arid and semi-arid areas are data-scarce aerosol areas. We provide path-breaking, high-resolution, full coverage, and long time series AOD datasets (FEC AOD) to support the atmosphere and related studies in northwestern China. The FEC AOD effectively compensates for the deficiency and constraints of in situ observations and satellite AOD products. Meanwhile, FEC AOD products demonstrate a reliable accuracy and ability to capture long-term change information.